Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

Fourier transform infrared (FTIR) spectroscopy has been advocating a promising alternative for Karl Fischer titration method for quantification of moisture in oil. This study aims to integrate partial least squares regression (PLSR) approach on FTIR spectra for prediction of moisture in locally accessible transformer oil and lubricating oil. The oil samples spiked with known moisture concentrations were extracted with acetonitrile and subjected to analysis with an FTIR spectrophotometer. The PLSR model was built based on 100 training/test splits, and the prediction performance was measured with the percentage root mean squares error (% RMSE). The range of concentration studied was between 0 and 5000 ppm. The marker region of moisture was found at 3750–3400 and 1700–1600 cm−1 with the latter demonstrating a better predictive ability in both lubricating oil and transformer oil. The prediction of moisture in lubricating oil was characterized with lower % RMSE. At concentration less than 700 ppm, the prediction accuracy deteriorates suggesting poor sensitivity. The PLSR was implemented on IR spectra of a set of blind samples, verified with Karl Fischer (for transformer oil) method and Kittiwake (for lubricating oil) method. The prediction was encouraging at concentrations above 1000 ppm; at lower concentrations, the prediction was characterized with high percent error. The algorithm, validated with 100 training/test splits, was converted into an executable program for prediction of moisture based on FTIR spectra. This program can be used for prediction of other substances given that the marker region is identified. FTIR can be used for prediction of moisture in oil nevertheless the sensitivity and precision is low for samples with low moisture concentration.

This study reports the caffeine content in seven locally available coffee. The caffeine was extracted with chloroform and analysed using Fourier Transform Infrared (FTIR). The method reports an average recovery of 101% with the limit of determination established at 0.1%. The absorption band at 1654 cm-1 was used to construct the calibration curve for quantification of caffeine where the regression was fitted with satisfactory linearity. An average of 0.55% of caffeine was detected in the seven coffee products with Arabica coffee demonstrating lower caffeine concentration. The study evidenced that caffeine content in coffee is determined by the coffee types. The caffeine content found in the local coffee products was relatively lower likely due to the solvent types, extraction procedure and analytical method used. Keywords: Arabica coffee, decaffeinated, chloroform extraction, Robusta coffee